Method for providing a signal quality degree associated with an analyte value measured in a continuous monitoring system

ABSTRACT

A method providing a signal quality degree associated with an analyte value measured in a continuous monitoring system is disclosed. The method includes: receiving a measured analyte value from a biosensor; determining at least two impact parameters, wherein each of the impact parameters is influenced by an operational status of the continuous monitoring system and wherein each of the impact parameters is capable of exerting an influence on the signal quality of the biosensor and wherein the influence of each of the impact parameters on the signal quality of the biosensor is expressed by a weight assigned to each of the impact parameters; and determining the signal quality degree associated with the measured analyte value as a function of the weights and the corresponding impact parameters; and providing the signal quality degree associated with the analyte value. A method of calibration using the signal quality degree is also disclosed.

RELATED APPLICATIONS

This application is a continuation of U.S. Ser. No. 16/229,322 filed onDec. 21, 2018 which is a continuation of PCT/EP2017/065942, filed onJun. 28, 2017, which claims priority to EP 16 176 940.1, filed on Jun.29, 2016, the entire disclosures of all of which are hereby incorporatedherein by reference.

BACKGROUND

The present disclosure relates to a method for providing a signalquality degree associated with an analyte value measured in a continuousmonitoring system as well as to related methods for determining anamount of insulin to be delivered and for calibrating the continuousmonitoring system. The present disclosure further relates to a computerprogram product as well as to a sensor unit and to a continuousmonitoring system which apply at least one of the mentioned methods.

The methods and devices according to the present disclosure mayprimarily be used for a continuous monitoring of the analyte glucose,wherein analyte values are measured by a biosensor in an interstitialfluid subcutaneously and/or in vivo, wherein the biosensor isimplantable or partially implantable. The methods and devices accordingto the disclosure may be applied both in the field of home care as wellas in the field of professional care, such as in hospitals. However,other applications are also feasible.

Monitoring certain body functions, more particularly monitoring one ormore concentrations of certain analytes, plays an important role in theprevention and treatment of various diseases. Without restrictingfurther possible applications, the methods and devices according to thedisclosure are described in the following with reference to a continuousmonitoring of the analyte glucose in an interstitial fluid by using abiosensor.

The glucose monitoring may be performed by using electrochemical sensorsas well as optical measurements. Examples of electrochemical biosensorsfor measuring glucose, specifically in blood or other body fluids, areknown from U.S. Pat. Nos. 5,413,690 A, 5,762,770 A, 5,798,031 A,6,129,823 A and US 2005/0013731 A1. For example, an active sensor regionis directly applied to a measurement site which is, generally, arrangedin an interstitial tissue, and may convert glucose into an electricallycharged entity by using an enzyme, in particular into glucose oxidase,generally abbreviated to “GOD.” As a result, the detectable charge inthe electrochemical biosensor may be related to the glucoseconcentration and can, thus, be used as a measurement variable. Examplesof such kinds of transcutaneous measurement systems are described inU.S. Pat. No. 6,360,888 B1 or US 2008/0242962 A1.

As generally known, glucose measurements may be performed as “spotmeasurements.” For this purpose, a sample of a body fluid is taken froma user, i.e., a human or an animal, in a targeted fashion and examinedwith respect to the analyte concentration in vitro and/or in atransdermal fashion. In contrast, the continuous measuring of theanalyte glucose in the interstitial fluid, also referred to as“continuous glucose monitoring” or abbreviated to “CGM,” has beenestablished as a method for managing, monitoring, and controlling adiabetes state. For this purpose, the continuous measuring of theanalyte value in the interstitial fluid is performed via atranscutaneous or a subcutaneous system in a subcutaneous fashion and/orin vivo. Accordingly, the biosensor or at least a measuring portionthereof may be arranged under the skin of the user. Generally, anevaluation and control part of the system, also referred to as a“patch,” may be located outside the body of a user. The biosensor isgenerally applied by using an insertion instrument, which is, in anexemplary fashion, described in U.S. Pat. No. 6,360,888 B1. However,other types of insertion instruments are also known. Further, a controlpart may be required. Such a control part may be located outside thebody and have to be in communication with the biosensor. Generally,communication is established by providing at least one electricalcontact between the biosensor and the control part, wherein the contactmay be a permanent electrical contact or a releasable electricalcontact. Other techniques for providing electrical contacts, such as byusing appropriate spring contacts, are known and may also be applied.

In continuous glucose measuring systems, the concentration of theanalyte glucose may be determined by employing an electrochemical sensorcomprising an electrochemical cell having a working electrode and acounter electrode. Herein, the working electrode may have a reagentlayer comprising an enzyme with a redox active enzyme co-factor adaptedto support an oxidation of the analyte in the body fluid. The reagentlayer may, further, comprise or redox mediator which, typically, may actas an electron acceptor. The redox mediator can react with the enzymeco-factor and may, thus, transport electrons received from the enzymeco-factor to a counter electrode surface, such as by diffusion. At thecounter electrode surface, the redox mediator may be oxidized and thetransferred electrons can, consequently, be detected as a current. Thecurrent may, preferably, be related to a concentration of the analyte inthe body fluid, e.g., such as being proportional thereto. US2003/0146113 A1 and US 2005/0123441 A1 disclose examples for thisprocess.

According to S. Shanthi and D. Kumar, Neural Network Based Filter forContinuous Glucose Monitoring: Online Tuning with Extended Kalman FilterAlgorithm, WSEAS Transactions on Information Science and ApplicationsVol. 9, 2012, p. 199-209, an evaluation of the accuracy of continuousglucose monitoring (CGM) systems is complex for two primary reasons.First, the CGM systems assess fluctuations of the blood glucose levelindirectly by measuring the concentration of interstitial glucose butare calibrated via self-monitoring in order to approximate the bloodglucose level. Second, CGM data reflect an underlying process in timeand usually consist of ordered-in-time highly interdependent datapoints. Apart from a physiological time lag and an improper calibration,random noise and errors, in particular due to sensor physics and sensorchemistry, might affect the accuracy of the CGM data. As a result, theperformance of CGM signals, in particular with respect to a hypoglycemicalert generation and to a control input into an artificial pancreas, maybe deteriorated. Related studies have shown that the percentage of falsealarms and missing alarms is about 50 percent, which the authorsprimarily assign to insufficient filtering.

US 2008/249384 A1 discloses glucose monitoring systems for continuouslymeasuring the glucose concentration in a patient's blood. The system isadapted to communicate with one or more sensors for transcutaneousinsertion into a patient and for producing sensor signals related to theglucose concentration. The system comprises an electronic calculatorunit and a display for displaying the measured glucose concentration.The electronic calculator unit further comprises means for calculatingan estimate of the uncertainty, i.e., the degree of accuracy of theglucose measurement, and the display is configured for displaying aninterval representing the uncertainty.

US 2005/004439 A1 discloses a method of calibrating glucose monitor dataincluding collecting the glucose monitor data over a period of time atpredetermined intervals. It also includes obtaining at least tworeference glucose values from a reference source that temporallycorrespond with the glucose monitor data obtained at the predeterminedintervals. Also included is calculating the calibration characteristicsusing the reference glucose values and corresponding glucose monitordata to regress the obtained glucose monitor data. And, calibrating theobtained glucose monitor data using the calibration characteristics. Inpreferred embodiments, the reference source is a blood glucose meter,and the at least two reference glucose values are obtained from bloodtests. In additional embodiments, calculation of the calibrationcharacteristics includes linear regression and, in particularembodiments, least squares linear regression. Alternatively, calculationof the calibration characteristics includes non-linear regression. Dataintegrity may be verified and the data may be filtered.

US 2014/121989 A1 discloses systems and methods for measuring an analytein a host. More particularly, the disclosure relates to systems andmethods for processing sensor data, including calculating a rate ofchange of sensor data and/or determining an acceptability of sensor orreference data.

US 2012/215462 A1 discloses systems and methods for processing sensoranalyte data, including initiating calibration, updating calibration,evaluating clinical acceptability of reference and sensor analyte data,and evaluating the quality of sensor calibration. During initialcalibration, the analyte sensor data is evaluated over a period of timeto determine stability of the sensor. The sensor may be calibrated usinga calibration set of one or more matched sensor and reference analytedata pairs. The calibration may be updated after evaluating thecalibration set for best calibration based on inclusion criteria withnewly received reference analyte data. Fail-safe mechanisms are providedbased on clinical acceptability of reference and analyte data andquality of sensor calibration. Algorithms provide for optimizedprospective and retrospective analysis of estimated blood analyte datafrom an analyte sensor.

S. Shanthi et al., s. o., deal with a removal of errors due to variousnoise distributions in CGM sensor data. A feed forward neural network istrained with an Extended Kalman Filter algorithm to nullify the effectsof white Gaussian, exponential and Laplace noise distributions in CGMtime series. The process and measurement noise covariance valuesincoming signal. This approach answers for an inter-person andintra-person variability of blood glucose profiles. The neural networkupdates its parameters in accordance with a signal-to-noise-ratio of theincoming signal. The performance of the proposed system is analyzed withroot mean square as a metric and has been compared with previousapproaches in terms of time lag and smoothness relative gain. The newmechanism enables the application of CGM signals to hypoglycemic alertgeneration and input to an artificial pancreas.

US 2014/182350 A1 discloses a method for determining an end of life of aCGM sensor which includes evaluating a plurality of risk factors usingan end of life function to determine an end of life status of the sensorand providing an output related to the end of life status of the sensor.Thus, this method is directed to solving the problem of determining thestatus or time for which the end of life of a sensor is near, so that auser may be informed that the sensor should be changed. The plurality ofrisk factors are selected from a list including a number of days thesensor has been in use, whether there has been a decrease in signalsensitivity, whether there is a predetermined noise pattern, whetherthere is a predetermined oxygen concentration pattern, and an errorbetween reference BG values and EGV sensor values. For this purpose,quality metrics are scaled according to pre-determined weights andcombined to produce an indicator of the overall quality of the computedglucose value, wherein the weights may be applicable to every metric andmay show how indicative a metric is of end of life.

The present disclosure provides a method for providing a signal qualitydegree associated with an analyte value measured in a continuousmonitoring system, a method for determining an amount of insulin to bedelivered, a method for calibrating the continuous monitoring system, acomputer program product, a sensor unit, and a continuous monitoringsystem which at least partially avoid the shortcomings of known methodsand devices of this kind and which at least partially address theabove-mentioned challenges.

In particular, it is desired that the methods and devices according tothe present disclosure may be capable of providing a signal qualitydegree associated with a measured glucose which can be used in adecision whether an actually measured glucose value to which the signalquality degree is associated with may be considered in providing aspecific signal by the continuous monitory system or not. For thispurpose, it is, particularly, desired to implement a process which maybe adapted of consecutively acquiring measured data and providingassociated signal quality information, preferably in a nearly real,real-time or quasi-continuous approach, especially without userinteraction, during the lifetime of the biosensor. The signal qualitydegree may also be able to assume a value between 0 and 1, wherein thevalue of 0 describes an insufficient quality while the value of 1 refersto a sufficient quality. In particular, it is desired that the signalquality degree may allow providing an improved accuracy value for use asa control input into an artificial pancreas and/or for a hypoglycemicalert generation throughout the lifetime of the biosensor.

SUMMARY

The present application discloses a method for providing a signalquality degree associated with an analyte value measured in a continuousmonitoring system, a method for determining an amount of insulin to bedelivered, a method for calibrating the continuous monitoring system, acomputer program product, a sensor unit, and a continuous monitoringsystem. The disclosed methods and systems may additionally include theindividual features or various combination of the features describedherein.

As used in the following, the terms “have,” “comprise” or “include” orany arbitrary grammatical variations thereof are used in a non-exclusiveway. Thus, these terms may both refer to a situation in which, besidesthe feature introduced by these terms, no further features are presentin the entity described in this context and to a situation in which oneor more further features are present. As an example, the expressions “Ahas B,” “A comprises B” and “A includes B” may both refer to a situationin which, besides B, no other element is present in A (i.e., a situationin which A solely and exclusively consists of B) and to a situation inwhich, besides B, one or more further elements are present in entity A,such as element C, elements C and D or even further elements.

Further, it shall be noted that the terms “at least one,” “one or more”or similar expressions indicating that a feature or element may bepresent once or more than once typically will be used only once whenintroducing the respective feature or element. In the following, in mostcases, when referring to the respective feature or element, theexpressions “at least one” or “one or more” will not be repeated,non-withstanding the fact that the respective feature or element may bepresent once or more than once.

Further, as used in the following, the terms “preferably,” “morepreferably,” “particularly,” “more particularly,” “specifically,” “morespecifically” or similar terms are used in conjunction with optionalfeatures, without restricting alternative possibilities. Thus, featuresintroduced by these terms are generally optional features and are notintended to restrict the scope of the invention in any way. The variousembodiments may, as the skilled person will recognize, be performed byusing alternative features. Similarly, features introduced by “in anembodiment” or similar expressions are intended to be optional features,without any restriction regarding alternative embodiments, without anyrestrictions regarding the scope of the invention and without anyrestriction regarding the possibility of combining the featuresintroduced in such way with other optional or non-optional features.

In a first embodiment, a method for providing a signal quality degreeassociated with an analyte value measured in a continuous monitoringsystem is disclosed. Herein, the method comprises the following methodsteps as listed below:

-   -   a) receiving a measured analyte value from a biosensor, wherein        the biosensor is adapted for measuring the analyte values, and        wherein the biosensor is comprised in a continuous monitoring        system or controlled by the continuous monitoring system;    -   b) determining at least two impact parameters, wherein each of        the impact parameters is influenced by an operational status of        the continuous monitoring system, and wherein each of the impact        parameters is capable of exerting an influence on a signal        quality of the biosensor, wherein the influence of each of the        impact parameters on the signal quality of the biosensor is        expressed by a weight being assigned to each of the impact        parameters; and    -   c) determining the signal quality degree associated with the        measured analyte value by combining the weights and the        corresponding impact parameters; and providing the signal        quality degree associated with the analyte value.

The indicated steps may, preferably, be performed in the given order,thereby starting with step a). However, any or all of the indicatedsteps may also be preformed at least partially concurrently, such asover a definite period of time. Additionally, the indicated steps as awhole may also be repeated several times in order to achieve asubsequent determination of the signal quality degree, such as after aprespecified time or in consequence of an occurrence of a prespecifiedevent. Further, additional method steps, whether described herein ornot, may be performed, too.

The methods according to the present disclosure may becomputer-implemented methods. As generally used, the term“computer-implemented” indicates that performing the method involvesusing a processing module, such as a processing module as comprised in acomputer, in a computer-assisted system, in a computer network, or inanother programmable apparatus, whereby any or all features of themethod steps may be performed by employing a computer program beingadapted for a use in the processing module. For the purpose of thepresent disclosure, the processing module may be comprised in thecontinuous monitoring system, may be controlled by the continuousmonitoring system, may be controlling the continuous monitoring system,and/or may be at least communicating with the continuous monitoringsystem. As will be explained later in more detail, the processing modulemay, thus, be comprised in an electronics unit and/or in a receiver ofthe continuous monitoring system.

According to step a) described above, the biosensor which is adapted formeasuring the analyte values consecutively provides a plurality ofmeasured analyte values which are received by the continuous monitoringsystem. As generally used, the terms “biosensor” or “CGM sensor” referto an arbitrary device being configured for conducting at least onemedical analysis. For this purpose, the biosensor may be an arbitrarydevice configured for performing at least one diagnostic purpose and,specifically, comprising at least one analyte sensor for performing theat least one medical analysis. The biosensor may, specifically, comprisean assembly of at least two components being capable of interacting witheach other, such as in order to perform one or more diagnostic purposes,such as in order to perform the medical analysis. The components may becapable of performing at least one detection of the at least one analytein the interstitial fluid and/or in order to contribute to the at leastone detection of the at least one analyte in the interstitial fluid.Further, the biosensor may be connectable to an evaluation device, suchas to an electronics unit. For the purposes of the present disclosure,the biosensor may either constitute a part of the continuous monitoringsystem or not. In the latter case the biosensor may, however, becontrolled by the continuous monitoring system, for example, by using areceiver and/or an electronics unit of the continuous monitoring system.

In one embodiment, the biosensor may be a fully implantable biosensor ora partially implantable biosensor which may be adapted for performingthe detection of the analyte in the body fluid in a subcutaneous tissue,for example, in an interstitial fluid. As used herein, the terms“implantable biosensor” or “transcutaneous biosensor” may refer to anarbitrary biosensor being adapted to be fully or at least partlyarranged within the body tissue of the patient or the user. For thispurpose, the biosensor may comprise an insertable portion. Herein, theterm “insertable portion” may generally refer to a part or component ofan element configured to be insertable into an arbitrary body tissue.The biosensor may fully or partially comprise a biocompatible surface,i.e., a surface which may have as little detrimental effects on theuser, the patient, or the body tissue as possible, at least duringtypical durations of use. For this purpose, the insertable portion ofthe biosensor may have a biocompatible surface. As an example, thebiosensor, specifically the insertable portion thereof, may fully orpartially be covered with at least one biocompatible membrane, such as apolymer membrane or a gel membrane which, on one hand, may be permeablefor the body fluid or at least for the analyte as comprised therein andwhich, on the other hand, may retain sensor substances, such as one ormore test chemicals within the sensor, thus preventing a migrationthereof into the body tissue. Other parts or components of the biosensormay remain outside of the body tissue.

As generally used here, the term “patient” refers to a human being or toan animal, independent of whether the human being or the animal,respectively, may be in a healthy condition or may suffer from one ormore diseases. Further, the term “user” may refer to a human being,whether being the patient or not, or, to a computer-assisted systemwhich may be capable of receiving and/or interpreting any values,whether being measured values or values directly or indirectlydetermined therefrom. As an example, the user may be a human beingsuffering from diabetes or, in addition or as an alternative, a personor a computer-assisted system being in charge of supervising thepatient. Alternatively, or additionally, the disclosure may also beapplicable to other types of users or patients or diseases.

As used herein, the term “body fluid” may, generally, refer to a fluid,in particular a liquid, which may typically be present in a body or abody tissue of the user or the patient and/or which may be produced bythe body of the user or the patient. The body fluid may be selected fromthe group consisting of blood and interstitial fluid. However,additionally or alternatively, one or more other types of body fluidsmay be used, such as saliva, tear fluid, urine or other body fluids.During the detection of analyte, the body fluid may be present withinthe body or body tissue. Thus, the biosensor may, specifically, beconfigured for detecting the analyte within the body tissue.

As further used herein, the term “analyte” may refer to an arbitraryelement, component, or compound being present in the body fluid, whereinthe presence and/or the concentration of the analyte may be of interestto the user, the patient, or to a medical staff, such as to a medicaldoctor. Particularly, the analyte may be or may comprise at least onearbitrary chemical substance or chemical compound which may participatein the metabolism of the user or the patient, such as at least onemetabolite. As an example, the analyte may be selected from the groupconsisting of glucose, cholesterol, triglycerides, lactate. Additionallyor alternatively, however, other types of analytes may be used and/orany combination of analytes may be determined. The detection of theanalyte may be an analyte-specific detection. Without restrictingfurther possible applications, the present disclosure is discussedherein with reference to a continuous monitoring of glucose in aninterstitial fluid.

As used herein, the term “measured analyte value” refers to a result asacquired by a process of generating at least one signal, in particularat least one measurement signal, which characterizes an outcome of ameasurement with respect to a property of the analyte. According to thepresent disclosure, the measured analyte value is received from thebiosensor, wherein the term “receiving” refers to a process by which thecontinuous monitoring system obtains access to the at least one measuredanalyte value for further processing. Specifically, the at least onemeasured analyte value may be or may comprise at least one electronicsignal, such as at least one voltage signal and/or at least one currentsignal which may be transferred from the biosensor to the continuousmonitoring system. The at least one measured analyte value may be or maycomprise at least one analogue measured value and/or may be or maycomprise at least one digital measured value.

As used herein, the term “continuous monitoring” refers to a process ofconsecutively acquiring data and deriving desired information therefrom,preferably in a nearly real, real time or quasi-continuous approach, forfrequently providing and/or updating the measured analyte values, inparticular without user interaction. For this purpose, a plurality ofmeasured analyte values are generated and evaluated, wherefrom thedesired information is determined. The plurality of the measured analytevalues may be recorded within fixed or variable time intervals or,alternatively or in addition, at an occurrence of at least oneprespecified event. As an example, an analyte value may routinely berecorded every minute, wherein in the event that an actually recordedanalyte value may deviate from a previous analyte value above athreshold, the time interval may, however, be set to 10 seconds. Otherexamples depending on the circumstances of the patient or user may befeasible. In particular, the biosensor according to the presentdisclosure may be adapted for the continuous monitoring of one or moreanalytes, in particular of glucose, such as for managing, monitoring,and controlling a diabetes state.

According to step b) set forth above, at least two impact parameters aredetermined. As used herein, the term “determining” relates to a processof generating at least one representative result, such as a plurality ofrepresentative results, which may be acquired by evaluating the at leastone measured analyte value, wherein the term “evaluating” may refer toan application of methods for deriving the at least one representativeresult therefrom.

As used herein, the term “impact parameter” refers to a characteristicvalue of the continuous monitoring system, a part thereof, or acomponent which is controlled by the continuous monitoring system, forexample, the biosensor. Preferably, only those characteristic values areconsidered which, first, are influenced by an operational status of thecontinuous monitoring system and which, second, are able to exert aninfluence on a signal quality of the biosensor. Preferably, at least twoimpact parameters, alternatively, two, three, four, five, six or moreimpact parameters, are used for the determination of the signal qualitydegree which is associated with the measured analyte value. As describedbelow in more detail, such characteristic values are, preferably,considered which are capable of providing temporary information aboutthe operational status of the continuous monitoring system during thelifetime of the biosensor.

The influence of each of the impact parameters may exert on the signalquality of the biosensor is expressed by a weight that is assigned toeach of the impact parameters. As generally used, the term “weight”refers to a contribution each of the at least two impact parameters mayhave on the signal quality of the biosensor. Thus, each of the impactparameters may be adjusted with respect to its contribution toinfluencing the signal quality of the biosensor rather assuming an equalcontribution of all chosen impact parameters to a final result. Herein,the weight assigned to each impact parameter may be determined frompreviously recorded data and a corresponding variance thereof for therespective impact parameter. Thus, the weight may be expressed in formof a numerical value, such as a numeral having a positive or a negativesign. Examples for the weights may be found below. Alternatively, theweight may be expressed in form of a percentage, wherein the percentagesfor the weights for all selected input parameters may or may not sum upto 100%. However, other embodiments may be equally feasible. Inparticularly preferred embodiment, a specific weight may be assigned toeach of the selected impact parameters by employing a retrospectiveanalysis of selected measured analyte values as received from thebiosensor, such as by performing tests with the biosensor, inparticular, by acquisition of measured analyte values by using thebiosensor under known prespecified conditions of the continuousmonitoring system.

As used herein, the term “operational status” of the continuousmonitoring system refers to at least one measurable property of thecontinuous monitoring system, of a part thereof, or of a component beingcontrolled by the continuous monitoring system, e.g., the biosensor,wherein the measurable property is capable of temporarily influencing atleast one of the impact parameters. As a result, the operational statusof the continuous monitoring system may be subject to a temporalvariation, wherein the operational status which pertains simultaneouslyat the time at which the measured analyte value is received by thecontinuous monitoring system may, preferably, be denoted as “currentoperational status” and considered for the further processing, inparticular, for determining the amount of insulin to be provided to thepatient, such as by implementing a CGM augmented bolus. Consequently,the operational status of the continuous monitoring system is neitherlimited to an end of life status of the biosensor nor only capable ofproviding an output related to the end of life status of the biosensor.Rather, the operational status refers to, permanently or intermittently,provided information related to a temporary value of the at least onemeasurable property of the continuous monitoring system.

The at least two impact parameters may be selected from the followinglist which includes impact parameters which may be considered as beinginfluenced by the operational status of the continuous monitoringsystem:

-   -   at least one parameter related to a Kalman filter;    -   a current wear time of the biosensor;    -   a current age of the biosensor;    -   a current concentration range;    -   a deviation from a mean analyte value;    -   a current rate of change;    -   at least one quantity related to a calibration of the biosensor;    -   a current failure probability;    -   a current potential of a counter electrode;    -   at least one process parameter of the production of the        biosensor;    -   a sensitivity of the biosensor; and    -   at least one impedance value of the biosensor.

It may be emphasized that while all of the impact parameters in thislist are capable of providing information about the current operationalstatus of the continuous monitoring system, some of the impactparameters in this list appear intrinsically as not being capable ofproviding an output which could be related to the end of life status ofthe biosensor. In particular, the current failure probability, the atleast one process parameter of the production of the biosensor, and theat least one quantity related to the calibration of the biosensor,appear not capable of contributing to the end of life status of thebiosensor in a reasonable manner.

Investigations with existing continuous monitoring system have shownthat a selection of the at least two impact parameters from thefollowing list may be effective for determining the signal qualitydegree of the corresponding continuous monitoring system:

-   -   the (1,1) element of a covariance matrix of the Kalman filter;    -   the current rate of change;    -   the current potential of a counter electrode;    -   the current wear time of the biosensor;    -   a time passed since the last calibration of the biosensor; and    -   a sensitivity and/or admittance of the biosensor.

In a first embodiment, a parameter related to the covariance matrix of aKalman filter, in particular an element thereof, more particular the(1,1) matrix element thereof, may be selected as one of the impactparameters. However, the other matrix elements thereof may also be usedfor this purpose. As generally used, the term “Kalman filter” refers toan algorithm which uses a plurality of measured values that may comprisemeasurement inaccuracies and generates estimates of more accuratevariables compared to a single measurement alone. Preferably, thealgorithm may work in a two-step process, wherein, in a first step, theKalman filter may generate estimates of the current variables along withtheir accuracy. In a next step, values acquired in a subsequentmeasurement may be used for updating the estimates, such as by using aweighted average, wherein a higher weight may be assigned to estimatescomprising a higher certainty. Since the algorithm is recursive, it maybe used for real-time processing, whereby it may be sufficient to usethe present measured value, the previously estimated values, and anaccuracy matrix. Thus, the Kalman filter is applied in the field of dataprocessing, in particular as described in US 2012/191362 A1, the contentof which is incorporated here by reference. When applying the Kalmanfilter, the covariance matrix of the Kalman filter may be employed asone of the impact parameters for deriving the signal quality since itprovides a measure for an uncertainty in filtering.

In a further embodiment, the current wear time of the biosensor may beselected as one of the impact parameters. The current wear time may bedetermined by a time interval after an application of the biosensor tothe user. In this regard, the time of application of the biosensor tothe user may be determined by detecting the time when an electricalcircuit that involves the biosensor is completed. By way of example, byapplying a transmitter to a body mount which comprises the biosensor theelectrical circuit that involves the biosensor can be completed. Thecurrent wear time of the biosensor may be suitable for this purpose, inparticular, since the biosensor may comprise a membrane which may besoaked with interstitial fluid, to a large extent, in order to achievean enhancement of the analyte diffusion within the biosensor. In orderto allow determining a specific weight that may be assigned to thecurrent wear time as one of the selected impact parameters, theinfluence of the wear time may be determined in tests of the biosensor,which may, preferably, be performed in vitro, in vivo may also bepossible. As a result, the contribution of the current wear time to thesignal quality degree may be modeled as a function.

In a further embodiment, the current age of the biosensor may beselected as one of the impact parameters. The current age of thebiosensor may be determined by considering a time interval after theproduction of the biosensor has been concluded. The current age of thebiosensor may be suitable for this purpose since the biosensor may besubject to a degradation which may occur in one or more components ofthe biosensor independent of a frequency and/or a modality of its actualuse.

In a further embodiment, the deviation of the actually measured analytevalue from the mean analyte value may be selected as one of the impactparameters. As generally used, the term “mean analyte value” refers to amean value derived max which is derived from a number of alreadymeasured analyte values. The deviation from the mean analyte value maybe determined by comparing the measured analyte value with a mean valuewhich may be derived from a number of previously measured analytevalues. By way of example, an arithmetic mean may be considered, whereinan average obtained by the sum of the values divided by the number ofvalues may be determined. However, other kinds of means, such as amedian or geometric mean, may also be employed here.

In a further embodiment, the current concentration range with respect tothe actually measured analyte value may be selected as one of the impactparameters. The current concentration range may be determined by aconcentration of the analyte within in the interstitial fluid of theuser. The current concentration range may be suitable for this purposesince it may influence the signal quality in the following manner. Onone hand, a low analyte concentration may exhibit a low signal-to-noiseratio since less analyte may diffuse through the membrane of thebiosensor while an amount of one or more interferents may remainunmodified. As used herein, the low analyte concentration may refer to aconcentration range from 0 to 100 mg/dl, from 0 to 60 mg/dl, from 0 to50 mg/dl, or from 0 to 40 mg/dl. On the other hand, at very highconcentration ranges the sensor may enter a reaction product limitedregime in which the analyte may no longer be completely converted into acurrent. This effect may, in particular, occur in the case in whichglucose oxygen may be the reaction product. As used herein, the highconcentration may refer to a concentration range from 140 to 400 mg/dl,from 160 to 400 mg/dl, or from 200 to 400 mg/dl. Similarly to thecurrent wear time, a specific weight that may be assigned to the currentconcentration range as one of the selected impact parameters, may bedetermined in tests of the biosensor, which may be performed in vitroand wherein in vivo may also be possible. As a result, the contributionof the current concentration range to the signal quality degree may,thus, be modeled as a function of the current concentration.

In a further embodiment, the current rate of change of the actuallymeasured analyte value may be selected as one of the impact parameters.The current rate of change may be determined by recording a temporalalteration of the measured analyte value. In general, the current rateof change may provide a larger contribution to the inaccuracy of themeasured value the faster the measured value changes. Not wishing to bebound by theory, this effect may be based on an observation that a timelag between a concentration change in the interstitial fluid and thesubsequent concentration change in the blood may rise with increasingrate of change. Typically, any rate of change which may exceed a valueof 0.5 mg/(dl·min), of 1.0 mg/(dl·min), or of 2 mg/(dl·min), may betaken into account. Similarly as above, a specific weight that may beassigned to the current rate of change as one of the selected impactparameters may be determined in tests of the biosensor, which may,preferably, be performed in vitro and wherein in vivo may also bepossible. As a result, the contribution of the current rate of change tothe signal quality degree may, thus, be modeled as a function.

In a further embodiment, a parameter related to the calibration of thebiosensor may be selected as one of the impact parameters, such as thenumber of valid calibrations of the biosensor or the time which may havepassed since the last calibration. The time passed since the lastcalibration may be determined by recording the time at which acalibration event is performed, wherein the signal quality degree maydecrease with increasing time after the last calibration event.Alternatively or in addition, the number of valid calibration values maybe determined by counting calibration values which were successfullyobtained with the biosensor within a defined time interval in the past,preferably with little deviation and at slow rates of change.Additionally, it is possible to only consider such calibration values asvalid calibration values which have been acquired during a recent periodof time, wherein the term “recent” may refer to an adjacent period oftime, such as the last day, the last two days prior, or the last week tothe time of performing the method. Consequently, the signal quality maybe considered to increase with an increasing number of valid calibrationvalues.

In a further embodiment, the current failure probability of thebiosensor may be selected as one of the impact parameters. Herein, thecurrent failure probability may be determined by taking into account adropout probability of the biosensor, to which a value from 0 to 1 maybe assigned. A dropout which can directly be determined from a measuringsignal may occur in an event in which the measuring signal may decreaserapidly in a non-physiological manner. A higher value for the currentfailure probability may indicate that a dropout of the biosensor mayappear more likely, thus, resulting in a lower signal quality.

In a further embodiment in which the biosensor may be an electrochemicalsensor comprising an electrochemical cell having at least one workingelectrode and a counter electrode, wherein a predefined electricalpotential may be applied between the working electrode and the counterelectrode, the current potential of the counter electrode may beselected as one of the impact parameters. For this purpose, the currentpotential of the counter electrode may be determined by recording adeviation from the predefined electrical potential. Typically, thepotential at the counter electrode may be balanced versus the workingelectrode by using a potentiostat and keeping it at a constant level. Asused herein, the term “potentiostat” refers to an electronic devicebeing adapted for adjusting and/or measuring a voltage differencebetween the working electrode and the counter electrode within theelectrochemical cell. Alternatively, the electrochemical cell of thebiosensor may, additionally, include a reference electrode, wherein theelectrical potential of the working electrode may be kept constant withrespect to the reference electrode by using the potentiostat and whereinthe counter electrode may provide for a current compensation at theworking electrode. For this purpose, the reference electrode can bedriven within a typical operational mode comprising preset lower andupper limits as corresponding thresholds. Consequently, exceeding aprespecified threshold with respect to the preset limits may indicate alesser signal quality. Similarly as above, a specific weight that may beassigned to the potential of the counter electrode as one of theselected impact parameters may be determined in biosensor tests whichmay, preferably, be performed in vitro and wherein in vivo may also bepossible. As a result, the contribution of the potential of the counterelectrode to the signal quality degree may, thus, be modeled as afunction.

In this further embodiment, a sensitivity value and/or an impedancevalue related to the biosensor may be selected as one of the impactparameters. The “sensitivity value” may be determined by measuring a rawcurrent I of the biosensor, whereby a concentration c of an analyte,such as of glucose, may be taken into account. By way of example, thesensitivity S of the biosensor may show a linear relationship for theconcentration c, such as below an empirical value of 100 mg/dl to 150mg/dl for the analyte glucose but exhibit a more complex curvature forconcentrations above this empirical value. Further, the term “impedancevalue” may refer to at least one value derived from an impedancespectrum of the biosensor, in particular, one or more components of acomplex resistance, e.g., an admittance Y value at one or more differentfrequencies. It may be preferable to measure the DC raw current I fordetermining the sensitivity S of the biosensor while the complexadmittance Y or a value related thereto can, preferably, be determinedby using an AC circuit adapted for this purpose. However, one or moreother values as derived from the impedance spectrum may also befeasible.

By providing a relationship between the sensitivity S and the admittanceY of the biosensor, a sensitivity-to-admittance relation, such as aratio S/Y, may be determined. The sensitivity-to-admittance relationcan, preferably, be used to acquire information about a current state ofintrinsic transport properties of the membrane of the biosensor whilegeometric properties of the membrane, such as a swelling of the membraneduring the operation of the biosensor, can be disregarded. Thus, thesensitivity-to-admittance relation may remain constant during theoperation of the biosensor such that no in-vivo drift can occur in thebiosensor, as long as the biosensor is diffusion-controlled, i.e., aslong as a reaction rate of the analyte is considerably higher comparedto a diffusion rate of the analyte.

In addition to the sensitivity-to-admittance relation, further impactparameters may be used which can be determined by measuring theadmittance Y of the biosensor, in particular, an electrical capacitanceC of the biosensor, an electrical resistance R_(M) of the membrane,and/or a time constant r which may be obtained by the relationr=R_(M)·C. Further impact parameters may also be feasible.

According to step c) described above, a signal quality degree isdetermined by evaluating the measured analyte value by combining atleast two individual weights which are each assigned to a correspondingone of the at least two of the impact parameters. As generally used, theterms “signal quality degree” or “signal quality measure” refer to anumerical value which may be suitable to indicate an accuracy of theanalyte value as measured by the biosensor, wherein the accuracy of thebiosensor is usually considered as a closeness of the measured value ofa quantity of the analyte to a true value of the quantity of theanalyte. In this regard, the measured analyte values as provided by eachcontinuous monitoring system may each comprise a measurement error,wherein the measurement error may be defined as a relative standarddeviation (SRD) between the measured analyte values and correspondingreference analyte values. The relative standard deviation (SRD) may beone possible metric being applicable for representing the measurementerror. Alternatively or in addition, an absolute relative deviation(ARD), an absolute deviation (AD), or a standard deviation (SD) may alsobe employed for this purpose. As a result, the signal quality degree maybe reciprocally related to the measurement error that may be expectedfor a measured analyte value such that no expected measurement error maybe expressed as a high signal quality degree while a large expectedmeasurement error may be expressed as a low signal quality degree. Forpractical reasons, the signal quality degree may, preferably, assume anumerical value that may be selected from a predefined numerical range,such as between a lower threshold indicating complete inaccuracy and ahigher threshold indicating complete accuracy. By way of example, thesignal quality degree may assume a numerical value between 0 and 1 or,alternatively, between 0% and 100%. However, other relationships betweenthe signal quality degree and the accuracy may also be feasible.

In some embodiments, it may be advantageous to set the signal qualitydegree to a zero value, i.e., to indicate signal quality as completelyinaccurate, over a prespecified period of time in case a particularevent, in particular an event having a high priority, may occur.Consequently, the signal quality may already be set to zero in case oneof the high priority events assumes a zero value, thus, allowing a fasttreatment of specific events in determining the signal quality. Theprespecified period of time may be selected to at least cover a periodof time that may exceed the actual period of time during which the eventmay happen. This kind of event may include an initialization procedureof the biosensor and/or of the continuous measuring system, such as overa prespecified period of time after the continuous measuring systemand/or the biosensor may have been activated for measuring an analytevalue and/or until predefined operational conditions of the continuousmeasuring system and/or the biosensor may have been achieved. This kindof event may also include untypical voltage and/or current values at oneor more of the electrodes, such as unusual voltage values at the counterelectrode, non-physiologically increasing current values, or currentsignals exhibiting a poor signal-to-noise ration. Other kinds of eventsmay also be feasible. This embodiment may, thus, allow taking intoaccount in a simple manner the kind of deviations that may likely occurduring the corresponding event.

The accuracy of each of the measured analyte values may be determined ina reasonable approximation by combining the weights which areindividually assigned to each of the impact parameters and thecorresponding impact parameters. For this purpose, the weight for eachof the impact parameters may, in a particularly preferred embodiment, beassigned to the signal quality degree by using a multivariate function.As generally used, the term “multivariate function” refers to a functionadapted to derive at least one result from at least two variables. Forthis purpose, an arbitrary function for deriving at least one numericalresult, also referred to as an output, from at least two variables,preferably from the weights and the individually assigned impactparameters, also referred to as input variables, may be used. Thefunction may comprise an arbitrary rule for generating the output byusing the at least two input variables. The multivariate function is, ormay, comprise at least one equation, in particular a linear equationusing the at least two variables, the weights and the individuallyassigned impact parameters, and a plurality of coefficients, therebyderiving the at least one result. The multivariate function may be ormay include a one-step algorithm in which the weights and theindividually assigned impact parameters are used as input variables forone and the same algorithm, such as using one and the same equation.Alternatively, the multivariate function may be or may include multiplesteps, wherein, step-by-step, two or more algorithms are successivelyapplied.

In a particularly preferred embodiment, the signal quality degree Q maybe determined by using the multivariate function according to Equation(1):

Q=Σ ^(n) _(i) w _(i) ·p _(i),  (1)

wherein w_(i) denotes the i-th weight assigned to the i-th impactparameter p_(i), wherein the serial number i is a non-negative ascendingnatural number within a closed interval from 1 to n, wherein n denotesthe number of the selected impact parameters. In this manner it may beensured that an individual weight is assigned to each of the n impactparameters.

However, other kinds of combinations of the weights and thecorresponding impact parameters may also be feasible.

Thus, in accordance with step c), the signal quality degree is providedas a parameter which is associated with the analyte value. In aparticularly preferred embodiment, the signal quality degree determinedin a manner as described above and/or below may, subsequently, becommunicated to the user of the continuous monitoring system as atemporary parameter. In this regard, it may be particularly advantageousto, simultaneously or consecutively, communicate both the measuredanalyte value and the corresponding signal quality degree as will bedescribed below in more detail. Thus, the user obtains the signalquality degree as a parameter which, at any given event where it isdetermined, is temporarily associated with the corresponding analytevalue. As a result, the signal quality degree may be capable ofaccompanying the analyte value as measured throughout the lifetime ofthe biosensor, thereby, permanently or intermittently, providing anindication about the actual accuracy of the analyte value.

A method for determining an amount of insulin to be delivered isdisclosed herein. In one embodiment, the method comprises the followingmethod steps:

-   -   d) determining a signal quality degree by applying the method        for providing a signal quality degree associated with an analyte        value measured in a continuous monitoring system as described        herein; and    -   e) determining the amount of insulin from a data pair, wherein        the data pair comprises the measured analyte value from the        biosensor and the signal quality degree associated with the        measured analyte value.

The indicated steps may be performed in the given order, therebystarting with step d). However, any or all of the indicated steps mayalso be preformed at least partially concurrently, such as over adefinite period of time. Additionally, the indicated steps as a wholemay also be repeated several times in order to achieve a subsequentdetermination of the signal quality degree, such as after a prespecifiedtime or in consequence of an occurrence of a prespecified event.Further, additional method steps, whether described herein or not, mayalso be performed.

With respect to step d), reference may be made to the description of themethod for providing a signal quality degree associated with an analytevalue measured in a continuous monitoring system as described elsewherein this document. In this regard, it may be mentioned that step d),thereby includes an application of steps a), b) and c) as describedabove.

According to step e), the amount of insulin is determined from a datapair, wherein the data pair comprises the measured analyte value fromthe biosensor and the signal quality degree associated with the measuredanalyte value. As generally known, the term “insulin” refers to apeptide hormone as normally generated by beta cells in the pancreas usedfor regulating a metabolism of carbohydrates and fats by promoting anabsorption of glucose. The insulin is, however, used to medically treatone or more diseases, such as one or more kinds of diabetes mellitus,wherein the amount of insulin, which may also be denominated by theterms “dose” or “bolus,” to be provided to the patient requires carefulconsideration. For the purpose of determining the amount of insulin,especially, for mimicking an artificial pancreas, one or more algorithmsmay be used, in particular, a bolus calculator for determining a singledose of insulin, a pLGS (predictive low glucose suspend) algorithmand/or a CTR (control to range) algorithm.

As used herein, the term “data pair” refers to plurality of data,wherein each measured analyte value is directly coupled to theassociated signal quality degree in a fashion that both values areprovided as a doublet for further processing, in particular for failsafeoperation of the biosensor in the continuous monitoring system. Thus,not only the actual measured analyte value is taken into account fordetermining the actual amount of insulin to be provided to the patientbut also the associated signal quality degree. In case a low signalquality may be reported, it may be advantageous to suppress or antedatewarnings and/or alarms, particularly in order to avoid false alarms,thereby helping to prevent alarm fatigue in the patient. Taking intoaccount the signal quality in this manner may, in particular, allowsuppressing a hyperglycemic alarm being indicative of a, generally,non-hazardous excessive glucose concentration while a hypoglycemic alarmbeing indicative of an insufficient glucose concentration that maypotentially perilous to the patient may, equally, be antedated. Inregard of enhancing therapy decisions, further details may be found inUS 2011/184267 A1 and US 2014/100435 A1, the content of which areincorporated here by reference.

In some embodiments, the data pair may, simultaneously or consecutively,be communicated to a user. As already mentioned above, this may beadvantageous since not only the measured analyte value but also theassociated signal quality degree of a measured analyte value can,therefore, be used in a decision whether an actually measured analytevalue will be considered in providing a specific signal to thecontinuous monitoring system, e.g., for determining the amount ofinsulin to be provided to the patient. As a result, the signal qualitydegree may, thus, provide an improved accuracy value for the use as acontrol input into an artificial pancreas and/or for a hypoglycemicalert generation.

In a further embodiment, a method for calibrating the continuousmonitoring system is disclosed. This method comprises the steps ofdetermining at least one calibration factor by comparing at least onemeasured analyte value with a value for an analyte content and wherein asignal quality degree associated with the measured analyte value isdetermined by applying the method for determining the signal qualitydegree of an analyte value measured in a continuous monitoring system asdescribed elsewhere in this document. Thus, a general relationshipbetween the measured analyte value and the value for the analyte contentmay be acquired by considering the associated signal quality degree whenapplying the at least one calibration measurement. The generalrelationship can, for example, be reported in the form of one or morecalibration curves. In this connection, the general relationship is tobe understood to mean a rule for a plurality of different measuredanalyte values on the value for the analyte content, which ruledescribes how the value for the analyte content may influence themeasured analyte value. The rule can be ascertained for a continuousrange of values for the analyte content or else for a discontinuousrange of respective values, for example, a quantity of values spacedapart from one another. Accordingly, the general relationship can, forexample, include a pointwise assignment of multiple values, acalibration factor, a calibration curve, or calibration function to thecorresponding influence.

For calibrating, the at least one measured analyte value is, further,weighted pursuant to its associated signal quality degree. In aparticularly preferred embodiment, the at least one measured analytevalue may only be used for calibrating in a case in which its associatedsignal quality degree exceeds a predefined quality threshold. By way ofexample, the calibration of the continuous monitoring system may only beperformed in case the measured analyte value in question may exceed thepredefined quality threshold. Consequently, the calibration thecontinuous monitoring system may only be performed with high qualitymeasured analyte values. As a result, a smaller number of calibrationsmay be required for the continuous monitoring system.

In a further embodiment, at least two measured analytes values,preferably, two, three, four, five, six, eight, ten, or twelve measuredanalytes values, may be recorded and combined with their associatedsignal quality degrees. For this purpose, a mean value, preferably amedian, of the selected number of the measured analyte values may beformed by weighting each of the measured analyte values with acorresponding value that may be based on the associated signal qualitydegree. By way of example, a median of the ten measured analyte valuesmay be formed, whereby each of the ten measured analyte values may beweighted by its associated signal quality degree in order to be used forcalibration purposes.

Accordingly, the calibration factor may be determined by using previouscalibrations and weighing the calibration factors with the correspondingsignal quality degrees. Thus, the calibration may be performed bydetermining the desired values in a plurality of test samples orcalibration samples in which the value for the analyte content is known.For example, it may be possible to prepare test samples which have adefinite concentration of the known analyte and on which the selectedimpact parameter may exert a definite influence. In this way, it may bepossible to determine a quantity of triplets of values, which each maycomprise a pair of values, wherein each data pair comprises the measuredanalyte value and the value for the analyte content, and the associatedsignal quality degree. The pairs of values can themselves describe thegeneral relationship, or the general relationship can be ascertained,for example by means of a fit, wherein, the associated signal qualitymay be considered. In some cases, it may be possible, for the generalrelationship to be described by a straight line with respect to acertain axis, wherein the slope and axis intercept can be determined byusing an appropriate fit. The straight line may then be used as acalibration curve. More complex calibration curves may also be possible,for example exponential functions and/or polynomials, which may betterdescribe the mentioned relationship.

The general relationship, more particularly, the calibration curve orcalibration function, can be stored in at least one data storage, forexample, in a volatile and/or nonvolatile data storage, which may beconnected to at least one evaluation unit, such as a data processingdevice. The evaluation unit can be configured to completely or partlycarry out the method steps of the methods described herein. Thecalibration measurement can also be carried out in the evaluation unitor, alternatively, independently therefrom.

In another embodiment, a beneficial calibration interval may bedetermined by applying the signal quality degrees. As used herein, theterm “beneficial calibration interval” may refer to a time interval atthe end of which it may be recommended to perform a further evaluationprocedure of the continuous monitoring system. As a result, thecontinuous monitoring system may, thus, always operate in awell-calibrated fashion. Hereby, the calibration and, hence, theaccuracy of the continuous monitoring system can be enhanced.

Further, the beneficial calibration interval may be communicated to auser who may, thus, be capable of performing the further evaluationprocedure soon afterwards. Alternatively, the continuous monitoringsystem may automatically perform the further evaluation procedure afterthe beneficial calibration interval unless a critical situation of thepatient that may require immediately measuring further analyte valuestakes precedence.

Further, in an event in which the signal quality degree may fall below afirst predefined quality threshold, an alarm may be suppressed and onlyactivated after the signal quality degree meets a second predefinedquality threshold. Herein, the second predefined quality threshold maybe higher or lower compared to the first predefined quality threshold.Alternatively, in an event in which the signal quality degree may fallbelow a predefined quality threshold, an alarm may be antedated. Thesekinds of procedure may be advantageous in order to prevent alarm fatiguein the patient, wherein, the procedure may also depend on aconsideration whether false positive or false negative alarms constitutea major problem in the relevant situation.

With respect to the biosensor, the measured analyte value, the signalquality degree, and the continuous monitoring system, reference may bemade to the description of the method for providing a signal qualitydegree associated with an analyte value measured in a continuousmonitoring system as described elsewhere in this document.

In a further aspect of the disclosure, a sensor unit is described.Herein, the sensor unit comprises a biosensor, an electronics unit, anda mountable patch. With respect to the biosensor, reference may be madeto the description of the biosensor as described above and/or below.

As generally used, the term “electronics unit” refers to an arbitrarydevice having at least one electronic component. The electronics unit isadapted for performing any one of the methods according to the presentdisclosure, for example, the method for providing a signal qualitydegree associated with an analyte value measured in a continuousmonitoring system. For this purpose, the electronics unit may include atleast one electronic component for one or more of: performing ameasurement with the biosensor, such as performing a voltage measurementor a current measurement; recording sensor signals; storing measurementsignals or measurement data; and/or transmitting sensor signals ormeasurement data to another device. The electronics unit mayspecifically be embodied as a transmitter or may include a transmitterfor transmitting data. Other embodiments of the electronic componentsare feasible.

Further, the term “mountable patch” generally refers to a device beingadapted to receive both the biosensor and the electronics unit in afashion that the sensor unit may be arranged hereby. For this purpose,the mountable patch may exhibit a connected state or a disconnectedstate, wherein, in the disconnected state, the sensor unit may not beoperable. In the connected state, however, both the biosensor and theelectronics unit may be connected by the mountable patch in a mannerthat the sensor unit may then be operable. Further, since the biosensormay be a fully implantable biosensor or a partially implantablebiosensor adapted for performing the detection of the analyte in thebody fluid in a subcutaneous tissue, e.g., in an interstitial fluid, themountable patch may be arranged on the skin of the user. Thus, thesensor unit including the biosensor, the electronics unit, and themountable patch may, generally, be worn on the body of the patient.

In a further embodiment, a continuous monitoring system is disclosed.Herein, the continuous monitoring system comprises a sensor unit and areceiver, wherein the sensor unit comprises a biosensor, an electronicsunit, and a mountable patch, and wherein the electronics unit and/or thereceiver is adapted to perform the method for providing a signal qualitydegree associated with an analyte value measured in a continuousmonitoring system as described above. With respect to the sensor unit,the biosensor, the electronics unit, and the mountable patch, referencemay be made to the description of the sensor unit as described above.

As described above, the sensor unit including the biosensor, theelectronics unit, and the mountable patch may be considered a part of abody-worn portion of the continuous monitoring system. In contrast, thereceiver may, generally, be considered as a handheld and/or portableportion of the continuous monitoring system. In particular, the receivermay comprise one or more of a customized remote control or a smartphone.However, independent of their particular embodiments, the electronicsunit and/or the receiver are configured to perform the method steps asdescribed elsewhere in this document. Herein, at least one of theelectronics unit and the receiver may be operably connected to thebiosensor, wherein term “operably connected” may refer to a state inwhich two or more objects are connected to each other in a fashion thatthey can interact with each other. For example, the biosensor may beoperably connected to the electronics unit and/or to the receiver in amanner that the sensor signals of the biosensor may be transmitted tothe electronics unit and/or to the receiver, respectively. As usedherein, the term “operably connected” may also include an electricallyconductive connection, wherein the biosensor may be electricallyconnected via at least one of a conductive adhesive material or a plugconnection.

Further disclosed and proposed is a computer program includingcomputer-executable instructions for performing one or more of themethods according to the present disclosure in one or more of theembodiments enclosed herein when the program is executed on a computeror computer network. Specifically, the computer program may be stored ona computer-readable data carrier. Thus, one, more than one, or even allof the method steps as indicated above may be performed by using acomputer or a computer network, preferably by using a computer program.

Further disclosed and proposed is a computer program product havingprogram code means, in order to perform the method according to thepresent disclosure in one or more of the embodiments enclosed hereinwhen the program is executed on a computer, a computer-assisted system,or computer network. For example, the program code means may be storedon a computer-readable data carrier.

Further, the present application discloses and proposes a data carrierhaving a data structure stored thereon, which, after loading into acomputer, a computer-assisted system, or computer network, such as intoa working memory or main memory of the computer, the computer-assistedsystem or computer network may execute the method according to one ormore of the embodiments disclosed herein.

The present application further proposes and discloses a computerprogram product with program code means stored on a machine-readablecarrier, in order to perform the method according to one or more of theembodiments disclosed herein, when the program is executed on acomputer, a computer-assisted system, or computer network. As usedherein, the term “computer program product” refers to the program as atradable product. The product may generally exist in an arbitraryformat, such as in a paper format, or on a computer-readable datacarrier. For example, the computer program product may be distributedover a data network, such as the internet.

The present application further proposes and discloses a modulated datasignal which contains instructions readable by a computer system, acomputer-assisted system, or computer network, for performing the methodaccording to one or more of the embodiments as disclosed herein.

Preferably, referring to the computer-implemented aspects of thedisclosure, one or more of the method steps or even all of the methodsteps of the method according to one or more of the embodimentsdisclosed herein may be performed by using a computer, acomputer-assisted system, or computer network. Thus, generally, any ofthe method steps including provision and/or manipulation of data may beperformed by using a computer, a computer-assisted system, or a computernetwork. Generally, these method steps may include any of the methodsteps, typically except for method steps requiring manual work, such asproviding the samples and/or certain aspects of performing the actualmeasurements.

In a preferred embodiment, a remote control may, thus, be used as theportable device, wherein the computer program may be pre-installedand/or updated on the remote control. As used herein, the term “remotecontrol” may refer to a portable device of the continuous monitoringsystem which may be configured for wirelessly operating the continuousmonitoring system from a distance, particularly from a short distance,such as a few meters. The remote control may operate by usingdigitally-coded pulses of infrared radiation in order to control aplurality of functions of the continuous monitoring system. For thispurpose, the remote control may be a wireless handheld portable devicecomprising an array of buttons for adjusting the functions andcommunicating adjustments to the continuous monitoring system. Theremote control may also use both the at least one analyte value and thecorresponding associated signal quality degree for further dataprocessing, e.g., for determining the amount of insulin to be providedto the patient such as by implementing a CGM augmented bolus, i.e., adose whose value may be better adjusted to the actual requirements ofthe patient due to the continuous monitoring of the analyte value inconnection with the associated signal quality degrees. Further, theremote control may comprise a display on which data as received from thecontinuous monitoring system may be displayed, in particular one or moremeasured analyte values, the associated signal quality degrees, thebeneficial calibration interval, and or other data which may or may notbe related with the mentioned data. However, other arrangements may befeasible, such as ultrasonic radiation, motion sensor-enabledcapabilities, voice control, and/or Bluetooth connectivity.

Thus, the remote control may be configured for communicating with any orall components of the continuous monitoring system. In a particularlypreferred embodiment, however, the remote control may be configured forcommunicating only with a predetermined set of the components of thecontinuous monitoring system. This kind of arrangement may, inparticular, allow activating access for the user only to thepredetermined functions of the continuous monitoring system whereasother functions may be separately controllable.

In a preferred embodiment, a smartphone may be used as the portabledevice. As generally used, the term “smartphone” refers to a portabledevice for mobile or handheld use, usually comprising a mobile phone anda mobile operating system which may open the opportunity for usingfeatures as known from a personal computer operating system. Generally,the smartphone is equipped with a touchscreen for user interaction, isconfigured for running computer programs which are usually referred toas applications, abbreviated as “apps,” and is adapted for internetaccess. Further, the smartphone may have one or more of a camera, avideo camera, a voice recorder, speech recognition, near fieldcommunication, or an infrared blaster. As a result, the computer programconfigured for performing one or more of the methods described hereinmay, thus, be downloaded from the internet in form of an application.Thus, the computer program configured for performing the instructions isavailable on a smartphone, wherein the computer program may beconfigured for embedding the smartphone into the continuous monitoringsystem.

Consequently, the smartphone may communicate with the continuousmonitoring system via the mentioned application. For this purpose, thesmartphone may not only allow displaying data as received from thecontinuous monitoring system, adjusting the functions of the continuousmonitoring system, and communicating the adjustments to the continuousmonitoring system in a similar fashion as the remote control but alsoperforming any or all of the method steps as described elsewhere in thisdocument by using the application. However, further opportunities mayalso be feasible, such as displaying the data received from thecontinuous monitoring system by a voice output, and adjusting thefunctions of the continuous monitoring system via a microphone and voicerecognition. Further, devices which may be denoted by the terms“personal digital assistant,” “tablet computer” or “tablet” and whichexhibit a number of common features with the definition as provided heremay also be considered as a smartphone.

The methods described herein exhibit a number of advantages with respectto the prior art. The methods and devices disclosed herein are capableof consecutively acquiring data and deriving desired informationtherefrom, preferably in a nearly real, real time, or quasi-continuousapproach, for frequently providing and/or updating the measured analytevalues, in particular without user interaction. Consequently, the signalquality degree which is associated with a measured glucose value asdetermined by using the methods and devices described herein and, inparticular, provided together with the associated measured glucose valueto a user can, preferably, be used in a therapeutic decision and/or fora calibration in a more reliable manner compared to known continuousmonitoring systems. Further, the present methods may, based on theseconsiderations, additionally be used for monitoring a failsafe operationof the biosensor. Thus, the signal quality degree as determined here mayallow providing an improved accuracy value for use as a control inputinto an artificial pancreas and/or for a hypoglycemic alert generationthroughout the lifetime of the biosensor. Further, the calibration and,hence, the accuracy of the continuous monitoring system may also beenhanced.

Summarizing, the following embodiments are potential embodiments of thepresent invention. Other embodiments, however, are also feasible.

Embodiment 1

A method for providing a signal quality degree associated with ananalyte value measured in a continuous monitoring system, the methodcomprising the steps of:

-   -   a) receiving a measured analyte value from a biosensor, wherein        the biosensor is adapted for measuring the analyte values, and        wherein the biosensor is comprised in a continuous monitoring        system or controlled by the continuous monitoring system;    -   b) determining at least two impact parameters, wherein each of        the impact parameters is influenced by an operational status of        the continuous monitoring system, and wherein each of the impact        parameters is capable of exerting an influence on a signal        quality of the biosensor, wherein the influence of each of the        impact parameters on the signal quality of the biosensor is        expressed by a weight being assigned to each of the impact        parameters; and    -   c) determining the signal quality degree associated with the        measured analyte value by combining the weights and the        corresponding impact parameters; and providing the signal        quality degree associated with the analyte value.

Embodiment 2

The method according to the preceding Embodiment, wherein the method isa computer-implemented method.

Embodiment 3

The method according to any one of the preceding Embodiments, whereinany or all of the method steps are performed by using a processingmodule.

Embodiment 4

The method according to the preceding Embodiment, wherein the processingmodule is one or more of:

-   -   comprised in the continuous monitoring system;    -   controlled by the continuous monitoring system;    -   controlling the continuous monitoring system;    -   communicating with the continuous monitoring system.

Embodiment 5

The method according to the preceding Embodiment, wherein the processingmodule is comprised in an electronics unit of the continuous monitoringsystem.

Embodiment 6

The method according to any one of the preceding Embodiments, whereinboth the measured analyte value and the associated signal quality degreeare, simultaneously or consecutively, communicated to a user.

Embodiment 7

The method according to any one of the preceding Embodiments, whereinthe biosensor is an implantable sensor or a partially implantable sensorbeing indicative of the analyte glucose.

Embodiment 8

The method according to any one of the preceding Embodiments, furthercomprising the step of implanting the biosensor being indicative of theanalyte glucose into the skin of a user.

Embodiment 9

The method according to any one of the preceding Embodiments, whereinthe analyte value is measured by the biosensor in an interstitial fluid.

Embodiment 10

The method according to the preceding Embodiment, wherein theinterstitial fluid comprises blood of a user.

Embodiment 11

The method according to any one of the preceding Embodiments, whereinthe analyte value is measured by the biosensor subcutaneously and/or invivo.

Embodiment 12

The method according to any one of the preceding Embodiments, whereinthe analyte value is measured without user interaction.

Embodiment 13

The method according to any one of the preceding Embodiments, wherein aplurality of analyte values is measured by the biosensor.

Embodiment 14

The method according to the preceding Embodiment, wherein the pluralityof the analyte values is measured within one or more of:

-   -   a fixed time interval;    -   a variable time interval;    -   at an occurrence of at least one prespecified event.

Embodiment 15

The method according to any one of the preceding Embodiments, whereinthe weight is assigned to the impact parameter by a retrospectiveanalysis of selected measured analyte values from the biosensor.

Embodiment 16

The method according to any one of the preceding Embodiments, whereinthe weight is assigned to each of the impact parameters by using amultivariate function.

Embodiment 17

The method according to the preceding Embodiment, wherein the signalquality degree Q is determined by using the multivariate function

Q=Σ ^(n) _(i) w _(i) ·p _(i),  (1)

wherein w_(i) denotes the weight assigned to the impact parameter p_(i),and wherein i=1 to n, wherein n denotes a number of the selected impactparameters.

Embodiment 18

The method according to any one of the preceding Embodiments, whereinthe at least two impact parameters which are influenced by theoperational status of the continuous monitoring system are selectedfrom:

-   -   at least one parameter related to a Kalman filter;    -   a current wear time;    -   a current age of the biosensor;    -   a current concentration range;    -   a deviation from a mean analyte value;    -   a current rate of change;    -   at least one quantity related to a calibration of the biosensor;    -   a current failure probability;    -   a current potential of a counter electrode;    -   at least one process parameter of the biosensor production    -   a sensitivity of the biosensor; and    -   at least one impedance value of the biosensor.

Embodiment 19

The method according to the preceding Embodiment, wherein the at leasttwo impact parameters (136) are selected from:

-   -   the (1,1) element of a covariance matrix of the Kalman filter;    -   the current rate of change;    -   the current potential of a counter electrode;    -   the current wear time of the biosensor;    -   a time passed since the last calibration of the biosensor; and    -   a sensitivity and/or an admittance of the biosensor.

Embodiment 20

The method according to any one of the two preceding Embodiments,wherein the current wear time is determined by a time interval after anelectric circuit comprising the biosensor is completed.

Embodiment 21

The method according to any one of three the preceding Embodiments,wherein the current age of the biosensor is determined by a durationafter a completion of the biosensor.

Embodiment 22

The method according to any one of the four preceding Embodiments,wherein the deviation from the mean analyte value is determined bycomparing the measured analyte value with a mean value derived from anumber of previously measured analyte values.

Embodiment 23

The method according to any one of the five preceding Embodiments,wherein the current concentration range is determined by a concentrationof the analyte in the interstitial fluid of the user.

Embodiment 24

The method according to any one of the six preceding Embodiments,wherein the current rate of change is determined by recording a temporalalteration of the measured analyte value.

Embodiment 25

The method according to any one of the seven preceding Embodiments,wherein the number of valid calibrations is determined.

Embodiment 26

The method according to any one of the eight preceding Embodiments,wherein the current failure probability is determined from a probabilityof a failure of the biosensor.

Embodiment 27

The method according to any one of the nine preceding Embodiments,wherein the biosensor comprises an electrochemical cell having at leastone working electrode and a counter electrode, wherein a predefinedelectrical potential is applied between the working electrode and thecounter electrode.

Embodiment 28

The method according to the preceding Embodiment, wherein theelectrochemical cell further has at least one reference electrode,wherein the electrical potential of the working electrode is keptconstant with respect to the reference electrode by using thepotentiostat and wherein the counter electrode provides for a currentcompensation at the working electrode.

Embodiment 29

The method according to any one of the two preceding Embodiments,wherein a sensitivity S of the biosensor or a value related thereto isdetermined by measuring a raw current through the working electrode ofthe biosensor, whereby a concentration c of the analyte is taken intoaccount.

Embodiment 30

The method according to any one of the three preceding Embodiments,wherein a complex admittance Y of the biosensor or a value relatedthereto is determined by using an AC circuit.

Embodiment 31

The method according to any one of the two preceding Embodiments,wherein a relationship between the sensitivity S and the admittance Y ofthe biosensor, i.e., a sensitivity-to-admittance relation, isdetermined, wherein the sensitivity-to-admittance relation I used toacquire information about a current state of intrinsic transportproperties of the membrane of the biosensor, disregarding geometricproperties of the membrane.

Embodiment 32

The method according to any one of the three preceding Embodiments,wherein the impact parameter may further be selected from: an electricalcapacitance C of the surface of the biosensor, an electrical resistanceR_(M) of the membrane, or a time constant τ=R_(M)·C.

Embodiment 33

The method according to any one of the preceding Embodiments, whereinthe signal quality degree associated with the measured analyte value isa numerical value selected from a predefined numerical range.

Embodiment 34

The method according to any one of the preceding Embodiments, whereinthe signal quality is set to a zero value within an initializationprocedure.

Embodiment 35

A method for determining an amount of insulin to be delivered, themethod comprising the steps of:

-   -   d) determining a signal quality degree by applying a method        according to any one of the preceding Embodiments; and    -   e) determining the amount of insulin from at least one data        pair, wherein the data pair comprises the measured analyte value        from the biosensor and the signal quality degree associated with        the measured analyte value.

Embodiment 36

The method according to the preceding Embodiment, wherein the data pairis communicated to a user.

Embodiment 37

A method for calibrating a continuous monitoring system, wherein atleast one calibration factor is determined by comparing at least onemeasured analyte value with a value for an analyte content, wherein asignal quality degree associated with the measured analyte value isdetermined by applying the method for providing a signal quality degreeassociated with an analyte value measured in a continuous monitoringsystem, wherein the at least one measured analyte value is weightedpursuant to its associated signal quality degree.

Embodiment 38

The method according to the preceding Embodiment, wherein the at leastone measured analyte value is only used for calibrating in a case inwhich its associated signal quality degree exceeds a predefined qualitythreshold.

Embodiment 39

The method according to any one of the two preceding Embodiments,wherein at least two measured analytes value are combined with respectto their associated signal quality degrees.

Embodiment 40

The method according to the preceding Embodiment, wherein a mean valueof the at least two measured analyte values is formed by weighting eachof the measured analyte values with a value based on the associatedsignal quality degree for each of the measured analyte values.

Embodiment 41

The method according to the preceding Embodiment, wherein a median ofthe at least two measured analyte values is formed by weighting each ofthe measured analyte values with a value based on the associated signalquality degree for each of the measured analyte values.

Embodiment 42

The method according to any one of the five preceding Embodiments,wherein a beneficial calibration interval is determined by applying thesignal quality degrees, wherein the beneficial calibration interval iscommunicated to a user.

Embodiment 43

The method according to any one of the six preceding Embodiments,wherein, in an event in which the signal quality degree falls below apredefined quality threshold, an alarm is suppressed and activated afterthe signal quality degree meets a second predefined quality threshold.

Embodiment 44

The method according to any one of the seven preceding Embodiments,wherein, in an event in which the signal quality degree falls below apredefined quality threshold, an alarm is antedated.

Embodiment 45

A computer program product comprising executable instructions forperforming a method according to any one of the preceding Embodimentsreferring to a method.

Embodiment 46

The computer program product according to the preceding Embodiment,wherein a computer program configured for performing the instructions isavailable on a portable device.

Embodiment 47

The computer program product according to the preceding Embodiment,wherein the portable device is smartphone or a remote control.

Embodiment 48

The computer program product according to the preceding Embodiment,wherein the computer program is configured for embedding the smartphoneinto the continuous monitoring system.

Embodiment 49

A sensor unit, comprising a biosensor, an electronics unit, and amountable patch, wherein the electronics unit is configured forperforming at least one of the methods according to any one of thepreceding Embodiments referring to a method.

Embodiment 50

A continuous monitoring system, comprising a sensor unit and a receiver,wherein the sensor unit comprises a biosensor, an electronics unit, anda mountable patch, and wherein the electronics unit and/or the receiveris configured for performing at least one of the methods according toany one of the preceding Embodiments referring to a method.

BRIEF DESCRIPTION OF THE DRAWINGS

The above-mentioned aspects of exemplary embodiments will become moreapparent and will be better understood by reference to the followingdescription of the embodiments taken in conjunction with theaccompanying drawings, wherein:

FIG. 1 schematically illustrates courses of a method for providing asignal quality degree associated with an analyte value measured in acontinuous monitoring system and of a related method for determining anamount of insulin to be delivered; and

FIG. 2 illustrates a number of observable measurement errors which aredepicted versus the measurement error expressed as a relative standarddeviation (SRD).

DESCRIPTION

The embodiments described below are not intended to be exhaustive or tolimit the invention to the precise forms disclosed in the followingdetailed description. Rather, the embodiments are chosen and describedso that others skilled in the art may appreciate and understand theprinciples and practices of this disclosure.

FIG. 1 schematically illustrates a particularly preferred embodiment fora course of a method 110 for providing a signal quality degree 112associated with an analyte value 114 measured in a biosensor 116,wherein the biosensor 116 is comprised in a continuous monitoring system118 or wherein the biosensor 116 is controlled by the continuousmonitoring system 118. Irrespective of details of a relationship betweenthe biosensor 116 and the continuous monitoring system 118, thebiosensor 116 is adapted for measuring the analyte values of a user 120.

In this a particularly preferred embodiment, the biosensor 116 is afully implantable biosensor which is adapted for performing thedetection of the analyte in the body fluid in a subcutaneous tissue, inparticular, in an interstitial fluid. Accordingly, the implantable ortranscutaneous biosensor 116 is adapted to be fully arranged within thebody tissue of the user 120. For this purpose, the biosensor comprisesan insertable portion 122 configured to be insertable into the bodytissue. Preferably, the biosensor may fully or partially comprise abiocompatible surface, i.e., a surface which may have as littledetrimental effects on the user 120 or the body tissue as possible, atleast during typical durations of use. As an example, the biosensor 116,specifically the insertable portion 122 thereof, is fully or partiallybe covered with at least one biocompatible membrane, such as a polymermembrane or a gel membrane which, on one hand, is permeable for the bodyfluid, at least for the analyte comprised therein, and which, on theother hand, is adapted to retain sensor substances, such as one or moretest chemicals within the sensor, thus preventing a migration thereofinto the body tissue.

In this embodiment, the biosensor 116 is a part of a sensor unit 124which, apart from the biosensor 116, comprises an electronics unit 126and a mountable patch 128. Herein, the electronics unit 126 is adaptedfor performing the methods described herein, in particular, the method110 for determining the signal quality degree 112 of the analyte value114 measured in a continuous monitoring system 118. As schematicallyillustrated in FIG. 1, the mountable patch 128 is arranged on the skinof the user 120 and exhibits a connected state in which both thebiosensor 116 and the electronics unit 126 are connected via themountable patch 128 in a manner that the sensor unit 124 is operable.

In accordance with step a) of the method 110 for determining the signalquality degree 112 of the analyte value 114 measured in the biosensor116, the analyte value 114 as measured in a biosensor 116, also denotedas s, is received by a processing module 130, wherein the processingmodule 130 communicates with the continuous monitoring system 118.Preferably, a computer program 132, also denominated as an applicationor app, configured for performing the instructions of the method 110 isavailable on a smartphone 134, wherein the computer program 132 isconfigured in this embodiment for embedding the smartphone 134 into thecontinuous monitoring system 118. Consequently, the smartphone 134communicates with the continuous monitoring system 134 via the computerprogram 132 without interaction of the user 120.

In accordance with step b) of the method 110 for determining the signalquality degree 112 of the analyte value 114 measured in the biosensor116, at least two impact parameters 136, also denoted by p₁, aredetermined. An operational status of the continuous monitoring system118 exerts an influence 138 on the impact parameters 136, wherein eachof the impact parameters is capable of exerting an influence 140 on thesignal quality of the biosensor 116. An influence 142 of each of theimpact parameters 136 on the signal quality of the biosensor 116 isexpressed by a weight 144, also denoted by w_(i), wherein the weightw_(i) 144 is assigned to the impact parameter p_(i) 136.

In accordance with step c) of the method 110 for providing the signalquality degree 112 to the analyte value 114 as measured in the biosensor116, the signal quality degree 112, also denoted by Q and beingassociated with the measured analyte value s 114, is determined bycombining the weight w; 144 being assigned to each of the impactparameter p_(i) 136, and the corresponding impact parameter p_(i) 136,in particular by using the multivariate function according to Equation(1):

Q=Σ ^(n) _(i) w _(i) ·p _(i),  (1)

wherein w_(i) denotes the i-th weight 144 assigned to the i-th impactparameter p_(i) 136, wherein the serial number i is a non-negativeascending natural number within a closed interval from 1 to n, wherein ndenotes the number of the selected impact parameters 136. In thisfashion it can be ensured that the individual weight w_(i) 144 isassigned to each of the n impact parameters p_(i) 136.

The signal quality degree Q 112 as determined here may, subsequently, beprovided together with the analyte value s 114 a whereby it may becommunicated to the user 120. However, it may be advantageous to,consecutively or, preferably, simultaneously, communicate both themeasured analyte value s 114 and the corresponding signal quality degreeQ 112 as a data pair 146 to the user 120. For this purpose, the courseof a related method 148 for determining an amount of insulin 150 to bedelivered to the user 120 is further schematically illustrated inFIG. 1. Thus, in accordance with step e) of the method 148 fordetermining an amount of insulin 150 to be delivered, the amount ofinsulin 150 may also be determined from the data pair 146.

As generally known, the measured analyte values 114 as provided by eachcontinuous monitoring system 118, may comprise a measurement error 152,wherein the measurement error 152 is defined here by using a relativestandard deviation 154 between the measured analyte values 114 andcorresponding reference analyte values as a metric. However, asmentioned above, other kinds of metrics may also be applicable. Atypical example in which a number 156 of observable measurement errors152 is depicted versus the measurement error 152, which is expressed asa relative standard deviation (SRD) 154, is shown in FIG. 2.

The signal quality degree 112 associated with the measured analyte value114 may at least partially be determined by using additional informationin the form of at least two impact parameters 136, wherein each of theimpact parameters 136 is influenced by the operational status of thecontinuous monitoring system 118. Since the signal quality degree 112may, as mentioned above, be reciprocally related to the measurementerror 152 in a manner that no expected measurement error 152 may beexpressed as a high signal quality degree 112 while a large expectedmeasurement error 152 may be expressed as a low signal quality degree112, the number 156 of observable measurement errors 152 as, forexample, depicted in FIG. 2 may be used for determining the signalquality degree 112 of the analyte value 114 measured in the continuousmonitoring system 118.

Thus, the number 156 of the measurement errors 152 as illustrated inFIG. 2 can be correlated to a number of selected impact parameters 136that may be influenced by the operational status of the continuousmonitoring system 118. In the particular example shown in FIG. 2, thefollowing five impact parameters 136 _(p), i=1 to 5, have been takeninto account:

impact operational status of the parameter p_(i) continuous monitoringsystem p₁ covariance matrix of a Kalman filter p₂ current rate of changep₃ current potential of a counter electrode p₄ current wear time p₅ timepassed since last calibration of continuous monitoring system

Thus, in order to be able to predict the expected measurement errors152, a linear model which uses the additional information to calculatethe measurement error 152, has been fitted to the data as shown in FIG.2. In this particular example, the linear model employs the Equation (2)for determining the relative standard deviation (SRD):

$\begin{matrix}{{SRD} = {\sqrt{\left( \frac{{G(t)} - {R(t)}}{R(t)} \right)^{2}} = {{\sum_{i}{w_{i} \cdot p_{i}}} + c_{0} + ɛ}}} & (2)\end{matrix}$

wherein G(t) denotes the measured analyte (glucose) value 114 at thetime t, R(t) a reference analyte value at the same time t, p_(i) thecorresponding impact parameter, w_(i) the corresponding coefficientindicative of the weight of the respective impact parameter p_(i), and εthe remaining unexplained error, wherein the serial number i is anon-negative ascending natural number within a closed interval from 1 ton, wherein n denotes the number of the selected kinds of additionalinformation. Thus, Equation (2) which may be used to determine thesignal quality degree Q by using the impact parameter p_(i), and thecorresponding weight w_(i).

As mentioned above, the impact parameters 136 may, in this embodiment,be selected in the following manner. A first impact parameter p₁ couldbe derived from the (1,1) element of a covariance matrix of a Kalmanfilter according to Equation (3):

p ₁=√{square root over (P ₁₁)}/I  (3)

wherein P₁₁ denotes the (1,1) element of the covariance matrix of theKalman filter and wherein I denotes a current value in nA as derivedfrom the filter.

Further, a second impact parameter p₂ could be derived from the currentrate of change according to Equation (4):

p ₂ =|dG/dt|  (4)

wherein dG/dt denotes a current rate of change of the measured analyte(glucose) value 114 G(t) at the time t.

Further, a third impact parameter p₃ could be derived from a currentpotential of a counter electrode according to Equation (5):

$\begin{matrix}{p_{3} = \left\{ \begin{matrix}{low} & {U_{CE} < 850} \\{normal} & {850 \leq U_{CE} \leq {1050}} \\{high} & {U_{CE} > {1050}}\end{matrix} \right.} & (5)\end{matrix}$

wherein U_(CE) denotes the current voltage at the counter electrode.

Further, a forth impact parameter p₄ could be derived from a currentwear time of the biosensor 116 according to Equation (6):

$\begin{matrix}{p_{4} = \left\{ \begin{matrix}{{Early}\mspace{14mu} {phase}} & {{{Sensor}\mspace{14mu} {Use}\mspace{14mu} {Time}} < {3\mspace{14mu} {days}}} \\{{Late}\mspace{14mu} {phase}} & {{{Sensor}\mspace{14mu} {Use}\mspace{14mu} {Time}} \geq {3\mspace{14mu} {days}}}\end{matrix} \right.} & (6)\end{matrix}$

wherein the Sensor Use Time denotes the time of wear of the biosensor116 as described above in more detail.

Further, a fifth impact parameter p₅ could be derived from a time passedsince the last calibration of the biosensor 116 according to Equation(7):

p ₅ =t _(calibration)  (7)

wherein t_(calibration) denotes a time in minutes which has passed sincethe last calibration of the biosensor 116.

Thus, in the embodiment as illustrated in FIG. 2, five different kindsof impact factors p_(i), i=1 to 5, have been considered. However, inalternative embodiments, a further and/or another impact parameter may,alternatively or in addition, be taken into account.

Applying this kind of linear model to a set of clinical data as recordedby the corresponding continuous monitoring system 118, the followingresults for estimating the relative standard deviation (SRD) asillustrated in FIG. 2 have been obtained:

weight w_(i) estimated value standard error w₀ 18.661 3.1806 w₁ 23.1720.91556 w₂ 1.9827 0.17837 w₃ _(—) _(low) Baseline — w₃ _(—) _(normal)−10.981 3.1671 w₃ _(—) _(high) −9.9054 3.1753 w₄ _(—) _(late phase)−4.0178 0.24495 w₄ _(—) _(early phase) Baseline — w₅ 0.001518 0.00053343

Estimating the weights w_(i) for this kind of model may be applied forproviding an estimation about the typical measurement error 152 of acertain measured analyte value 114 by using the following impact factorsp_(i), i=1 to 5:

p₁ 0.12; p₂ 1.1 mg/dl/min p₃ normal p₄ early phase p₅ 800 minutes

As a result, the relative standard deviation (SRD) may be determinedaccording to Equation (8):

SRD=Σ^(n) _(i) w _(i) ·p_(i)=18.661+23.172·0.12+1.9827·1.1−10.981+0+0.001518·800=13.85  (8)

In a particular embodiment, 100% sensor quality degree may be defined asa relative standard deviation of 5 or less while 0% sensor qualitydegree may be defined as a relative standard deviation of 25 or more,any value of the relative standard deviation may be related to thesensor quality degree. In this particular example, the sensor qualitydegree can be determined according to Equation (9):

Q=13.85*100%/(25−5)=69.25%  (9)

While exemplary embodiments have been disclosed hereinabove, the presentinvention is not limited to the disclosed embodiments. Instead, thisapplication is intended to cover any variations, uses, or adaptations ofthis disclosure using its general principles. Further, this applicationis intended to cover such departures from the present disclosure as comewithin known or customary practice in the art to which this inventionpertains and which fall within the limits of the appended claims.

LIST OF REFERENCE NUMBERS

-   110 method for providing a signal quality degree associated with an    analyte value-   112 signal quality degree Q-   114 measured analyte value s-   116 biosensor-   118 continuous monitoring system-   120 user-   122 implantable biosensor-   124 sensor unit-   126 electronics unit-   128 mountable patch-   130 processing module-   132 computer program-   134 smartphone-   136 impact parameters p_(i)-   138 influence-   140 influence-   142 influence-   144 weight w_(i)-   146 data pair-   148 method for determining an amount of insulin-   150 amount of insulin-   152 measurement error-   154 relative absolute standard deviation-   156 number of observable measurement errors

What is claimed is:
 1. A method for calibrating a continuous monitoringsystem, wherein at least one calibration factor is determined bycomparing at least one measured analyte value with a value for ananalyte content, wherein a signal quality degree associated with themeasured analyte value is determined by applying a method for providinga signal quality degree associated with an analyte value measured in acontinuous monitoring system, and wherein the at least one measuredanalyte value is weighted pursuant to its associated signal qualitydegree.
 2. The method of claim 1 wherein the signal quality degreeassociated with the measured analyte value is determined by thefollowing steps: a) receiving a measured analyte value from a biosensor,wherein the biosensor is adapted for measuring the analyte values, andwherein the biosensor is included in the continuous monitoring system orcontrolled by the continuous monitoring system; b) determining at leasttwo impact parameters, wherein each of the impact parameters isinfluenced by an operational status of the continuous monitoring system,and wherein each of the impact parameters is capable of exerting aninfluence on a signal quality of the biosensor, wherein the influence ofeach of the impact parameters on the signal quality of the biosensor isexpressed by a weight assigned to each of the impact parameters; and c)determining the signal quality degree associated with the measuredanalyte value as a function of the weights and the corresponding impactparameters; and providing the signal quality degree associated with theanalyte value.
 3. The method for calibrating of claim 2 wherein at leasttwo measured analyte values are combined as a function of the associatedsignal quality degrees of the at least two measured analyte values. 4.The method for calibrating of claim 3 wherein the at least two measuredanalyte values are combined using one of the following steps: a) whereina mean value of the at least two measured analyte values is determinedby weighting each of the at least two measured analyte values with avalue based on the associated signal quality degree for each of themeasured analyte values; and b) wherein a median of the at least twomeasured analyte values is determined by weighting each of the at leasttwo measured analyte values with a value based on the associated signalquality degree for each of the measured analyte values.
 5. The method ofclaim 2, wherein the biosensor is an implantable sensor or a partiallyimplantable sensor being indicative of the analyte glucose, wherein theanalyte value is measured by the biosensor in an interstitial fluidsubcutaneously, and wherein the analyte value is measured withoutinteraction of a user.
 6. The method of claim 5, wherein the analytevalue is measured by the biosensor in vivo.
 7. The method of claim 2,wherein the weight is assigned to the impact parameter by aretrospective analysis of selected measured analyte values from thebiosensor and wherein the weight is assigned to each of the impactparameters by using a multivariate function.
 8. The method of claim 7,wherein the signal quality degree Q is determined by using themultivariate function:Q=Σniwi·pi,  (1) wherein wi denotes the weight assigned to the impactparameter pi, and wherein i=1, . . . , n, wherein n denotes a number ofthe impact parameters.
 9. The method of claim 2, wherein the at leasttwo impact parameters which are influenced by the operational status ofthe continuous monitoring system are selected from: at least oneparameter related to a Kalman filter; a current wear time of thebiosensor; a current age of the biosensor; a current concentrationrange; a deviation from a mean analyte value; a current rate of change;at least one quantity related to a calibration of the biosensor; acurrent failure probability; a current potential of a counter electrode;at least one process parameter of the production of the biosensor; asensitivity of the biosensor; and/or at least one impedance value of thebiosensor.
 10. The method of claim 9, wherein the at least two impactparameters are selected from: the (1,1) element of a covariance matrixof the Kalman filter; the current rate of change; the current potentialof a counter electrode; the current wear time of the biosensor; a timepassed since the last calibration of the biosensor; a sensitivity of thebiosensor; and/or an admittance of the biosensor.
 11. The method ofclaim 9, wherein one of the selected impact parameters is the currentwear time and wherein the current wear time is determined by a timeinterval after an application of the biosensor to the user.
 12. Themethod of claim 9, wherein one of the selected impact parameters is thecurrent age of the biosensor and wherein the current age of thebiosensor is determined by a duration after a completion of thebiosensor.
 13. The method of claim 9, wherein one of the selected impactparameters is the current concentration range and wherein the currentconcentration range is determined by a concentration of the analyte inthe interstitial fluid of the user.
 14. The method of claim 9, whereinone of the selected impact parameters is the deviation from the meananalyte value and wherein the deviation from the mean analyte value isdetermined by comparing the measured analyte value with a mean valuederived from a number of previously measured analyte values.
 15. Themethod of claim 9, wherein one of the selected impact parameters is thecurrent rate of change and wherein the current rate of change isdetermined by a recording a temporal alteration of the measured analytevalue.
 16. The method of claim 9, wherein one of the selected impactparameters is the number of calibrations and wherein the number ofcalibrations is determined by counting calibration procedures previouslyperformed with the biosensor.
 17. The method of claim 9, wherein one ofthe selected impact parameters is the current failure probability andwherein the current failure probability is determined from a probabilityof a failure of the biosensor.
 19. The method of claim 9, wherein one ofthe selected impact parameters is the current potential of the counterelectrode and wherein the biosensor comprises an electrochemical cellhaving at least one working electrode and the counter electrode, whereina predefined electrical potential is applied between the workingelectrode and the counter electrode, wherein the current potential ofthe counter electrode is determined by recording a deviation from thepredefined electrical potential, whereby at least one of the sensitivityand the admittance of the biosensor is measured.